dc.contributor.author | Balapuwaduge, Indika A. M. | |
dc.contributor.author | Li, Frank Yong | |
dc.date.accessioned | 2020-03-30T20:45:20Z | |
dc.date.available | 2020-03-30T20:45:20Z | |
dc.date.created | 2019-10-16T20:50:17Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Balapuwaduge, I. A. M. & Li, F. Y. (2019). Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks. IEEE International Conference on Communications. | en_US |
dc.identifier.isbn | 978-1-5386-8088-9 | |
dc.identifier.issn | 1938-1883 | |
dc.identifier.uri | https://hdl.handle.net/11250/2649532 | |
dc.description | Author's accepted manuscript. | en_US |
dc.description | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.description.abstract | Massive machine-type communication (mMTC) is expected to play a pivotal role in emerging 5G networks. Considering the dense deployment of small cells and the existence of heterogeneous cells, an MTC device can discover multiple cells for association. Under traditional cell association mechanisms, MTC devices are typically associated with an eNodeB with highest signal strength. However, the selected eNodeB may not be able to handle mMTC requests due to network congestion and overload. Therefore, reliable cell association would provide a smarter solution to facilitate mMTC connections. To enable such a solution, a hidden Markov model (HMM) based machine learning (ML) technique is proposed in this paper to perform optimal cell association. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2019 IEEE International Conference on Communications (ICC) | |
dc.title | Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2019 IEEE | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 6 | en_US |
dc.source.journal | IEEE International Conference on Communications | en_US |
dc.identifier.doi | https://doi.org/10.1109/ICC.2019.8761913 | |
dc.identifier.cristin | 1737807 | |
cristin.qualitycode | 1 | |